{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:FLPFHMKIPQFZXSUTEVUTTGG4PO","short_pith_number":"pith:FLPFHMKI","canonical_record":{"source":{"id":"1906.05186","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-06-12T14:57:21Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"c333bc79540529170effc72e34d6143e9c93573f7c102c34b216217167b75ade","abstract_canon_sha256":"337cb40cd715de4ae47461f3d533176fe2396b10131f142c7a2edf479dc507b8"},"schema_version":"1.0"},"canonical_sha256":"2ade53b1487c0b9bca9325693998dc7b98e7d482005a2c473ef7c79e215e8b36","source":{"kind":"arxiv","id":"1906.05186","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1906.05186","created_at":"2026-05-17T23:43:29Z"},{"alias_kind":"arxiv_version","alias_value":"1906.05186v1","created_at":"2026-05-17T23:43:29Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1906.05186","created_at":"2026-05-17T23:43:29Z"},{"alias_kind":"pith_short_12","alias_value":"FLPFHMKIPQFZ","created_at":"2026-05-18T12:33:15Z"},{"alias_kind":"pith_short_16","alias_value":"FLPFHMKIPQFZXSUT","created_at":"2026-05-18T12:33:15Z"},{"alias_kind":"pith_short_8","alias_value":"FLPFHMKI","created_at":"2026-05-18T12:33:15Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:FLPFHMKIPQFZXSUTEVUTTGG4PO","target":"record","payload":{"canonical_record":{"source":{"id":"1906.05186","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-06-12T14:57:21Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"c333bc79540529170effc72e34d6143e9c93573f7c102c34b216217167b75ade","abstract_canon_sha256":"337cb40cd715de4ae47461f3d533176fe2396b10131f142c7a2edf479dc507b8"},"schema_version":"1.0"},"canonical_sha256":"2ade53b1487c0b9bca9325693998dc7b98e7d482005a2c473ef7c79e215e8b36","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:43:29.253602Z","signature_b64":"iezIuLdkYketIxp7vBMPutJSZnLzi+Lbkts5ynfGJUo+gNDJGtt/lniT4xAKZlQvIJJPvSZU0nK0YxMsE1UxCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2ade53b1487c0b9bca9325693998dc7b98e7d482005a2c473ef7c79e215e8b36","last_reissued_at":"2026-05-17T23:43:29.253164Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:43:29.253164Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1906.05186","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:43:29Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Cr0IAIjF7C2HSg94sSbrEK2RSSMQ9Y64ZUCUimcfJbt+6hpjbcmYNkEmwjrPPC367h6v1D+z8KoABfnQ0wzqBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T01:07:58.492472Z"},"content_sha256":"2840ef6ea92d67b428051c573f33746cfc4363d22898e1a4f0c50da183b4bae9","schema_version":"1.0","event_id":"sha256:2840ef6ea92d67b428051c573f33746cfc4363d22898e1a4f0c50da183b4bae9"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:FLPFHMKIPQFZXSUTEVUTTGG4PO","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Boosting Few-Shot Visual Learning with Self-Supervision","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Andrei Bursuc, Matthieu Cord, Nikos Komodakis, Patrick P\\'erez, Spyros Gidaris","submitted_at":"2019-06-12T14:57:21Z","abstract_excerpt":"Few-shot learning and self-supervised learning address different facets of the same problem: how to train a model with little or no labeled data. Few-shot learning aims for optimization methods and models that can learn efficiently to recognize patterns in the low data regime. Self-supervised learning focuses instead on unlabeled data and looks into it for the supervisory signal to feed high capacity deep neural networks. In this work we exploit the complementarity of these two domains and propose an approach for improving few-shot learning through self-supervision. We use self-supervision as "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1906.05186","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:43:29Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"0wp/KqWf61eQNbt6v5mG0RQzeMmx3ANVwyC78ZvcnodbjgB5V64V1TWdpLswttkSuIMUCya4JOvauI6axdFEDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T01:07:58.493156Z"},"content_sha256":"aabeb44fc87c56dea0f54698081a6a8307ca52b49ebda047b675795d4ce141c3","schema_version":"1.0","event_id":"sha256:aabeb44fc87c56dea0f54698081a6a8307ca52b49ebda047b675795d4ce141c3"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/FLPFHMKIPQFZXSUTEVUTTGG4PO/bundle.json","state_url":"https://pith.science/pith/FLPFHMKIPQFZXSUTEVUTTGG4PO/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/FLPFHMKIPQFZXSUTEVUTTGG4PO/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-05-30T01:07:58Z","links":{"resolver":"https://pith.science/pith/FLPFHMKIPQFZXSUTEVUTTGG4PO","bundle":"https://pith.science/pith/FLPFHMKIPQFZXSUTEVUTTGG4PO/bundle.json","state":"https://pith.science/pith/FLPFHMKIPQFZXSUTEVUTTGG4PO/state.json","well_known_bundle":"https://pith.science/.well-known/pith/FLPFHMKIPQFZXSUTEVUTTGG4PO/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:FLPFHMKIPQFZXSUTEVUTTGG4PO","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"337cb40cd715de4ae47461f3d533176fe2396b10131f142c7a2edf479dc507b8","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-06-12T14:57:21Z","title_canon_sha256":"c333bc79540529170effc72e34d6143e9c93573f7c102c34b216217167b75ade"},"schema_version":"1.0","source":{"id":"1906.05186","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1906.05186","created_at":"2026-05-17T23:43:29Z"},{"alias_kind":"arxiv_version","alias_value":"1906.05186v1","created_at":"2026-05-17T23:43:29Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1906.05186","created_at":"2026-05-17T23:43:29Z"},{"alias_kind":"pith_short_12","alias_value":"FLPFHMKIPQFZ","created_at":"2026-05-18T12:33:15Z"},{"alias_kind":"pith_short_16","alias_value":"FLPFHMKIPQFZXSUT","created_at":"2026-05-18T12:33:15Z"},{"alias_kind":"pith_short_8","alias_value":"FLPFHMKI","created_at":"2026-05-18T12:33:15Z"}],"graph_snapshots":[{"event_id":"sha256:aabeb44fc87c56dea0f54698081a6a8307ca52b49ebda047b675795d4ce141c3","target":"graph","created_at":"2026-05-17T23:43:29Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"Few-shot learning and self-supervised learning address different facets of the same problem: how to train a model with little or no labeled data. Few-shot learning aims for optimization methods and models that can learn efficiently to recognize patterns in the low data regime. Self-supervised learning focuses instead on unlabeled data and looks into it for the supervisory signal to feed high capacity deep neural networks. In this work we exploit the complementarity of these two domains and propose an approach for improving few-shot learning through self-supervision. We use self-supervision as ","authors_text":"Andrei Bursuc, Matthieu Cord, Nikos Komodakis, Patrick P\\'erez, Spyros Gidaris","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-06-12T14:57:21Z","title":"Boosting Few-Shot Visual Learning with Self-Supervision"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1906.05186","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:2840ef6ea92d67b428051c573f33746cfc4363d22898e1a4f0c50da183b4bae9","target":"record","created_at":"2026-05-17T23:43:29Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"337cb40cd715de4ae47461f3d533176fe2396b10131f142c7a2edf479dc507b8","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-06-12T14:57:21Z","title_canon_sha256":"c333bc79540529170effc72e34d6143e9c93573f7c102c34b216217167b75ade"},"schema_version":"1.0","source":{"id":"1906.05186","kind":"arxiv","version":1}},"canonical_sha256":"2ade53b1487c0b9bca9325693998dc7b98e7d482005a2c473ef7c79e215e8b36","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"2ade53b1487c0b9bca9325693998dc7b98e7d482005a2c473ef7c79e215e8b36","first_computed_at":"2026-05-17T23:43:29.253164Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:43:29.253164Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"iezIuLdkYketIxp7vBMPutJSZnLzi+Lbkts5ynfGJUo+gNDJGtt/lniT4xAKZlQvIJJPvSZU0nK0YxMsE1UxCQ==","signature_status":"signed_v1","signed_at":"2026-05-17T23:43:29.253602Z","signed_message":"canonical_sha256_bytes"},"source_id":"1906.05186","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:2840ef6ea92d67b428051c573f33746cfc4363d22898e1a4f0c50da183b4bae9","sha256:aabeb44fc87c56dea0f54698081a6a8307ca52b49ebda047b675795d4ce141c3"],"state_sha256":"17f8d86a68431678f0de02a552eb861c6f4d4f06371c26388ac067290fac5e4f"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Dw7aM8kk7nofi+oF+7pfoDSBwVPdXdZOlvLhse3rvPRLqQdjfbgHwcAkkdNC2/GNcGWDjkrMhshWORqJP1BPDg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-30T01:07:58.496774Z","bundle_sha256":"5610be5490c104216ec8fb6923e5742ce86102f085a7fccf9e13defcc0cdfda8"}}